International Conference On Preconditioning Techniques For Scientific And Industrial Applications

19-21 June 2013
Oxford, UK

Conference Chairs

Esmond G. Ng, Lawrence Berkeley National Laboratory, USA
Yousef Saad, The University of Minnesota, USA
Wei-Pai Tang, The Boeing Company, USA
Andy Wathen, University of Oxford, UK

Local Organization (Mathematical Institute, University of Oxford)

General information for conference delegates

Click here to download a brief general information page.


Click here for the programme.


19-21 June 2013. Please note that this is the week before the 25th Biennial Numerical Analysis Conference at the University of Strathclyde in Glasgow.
The International Conference on Preconditioning Techniques for Scientific and Industrial Applications, Preconditioning 2013, is the eighth in a series of conferences that focus on preconditioning techniques in sparse matrix computation. Past Preconditioning Conferences were:
  • Preconditioning 1999, The University of Minnesota, Minneapolis, June 10-12 1999.
  • Preconditioning 2001, The Granlibakken Conference Center, Tahoe City, April 29 - May 1, 2001.
  • Preconditioning 2003, Embassy Suites Napa Valley, Napa, October 27-29, 2003.
  • Preconditioning 2005, Emory University, Atlanta, May 19-21, 2005.
  • Preconditioning 2007, Météopole, Toulouse, France, July 9-12, 2007.
  • Preconditioning 2009, Hong-Kong Baptist University, Hong-Kong, August 24-26, 2009.
  • Preconditioning 2011, Domaine du Haut-Carré, Bordeaux, France, May 16-18, 2011.

The goal of this series of conferences is to address the complex issues related to the solution of general sparse matrix problems in large-scale real applications and in industrial settings. The issues related to sparse matrix software that are of interest to application scientists and industrial users are often fairly different from those on which the academic community is focused. For example, for an application scientist or an industrial user, improving robustness may be far more important than finding a method that would gain speed. Memory usage is also an important consideration, but is seldom accounted for in academic research on sparse matrix solvers. As a last example, linear systems solved in applications are almost always part of some nonlinear iteration (e.g., Newton) or optimization loop. It is important to consider the coupling between the linear and nonlinear parts, instead of focusing on the linear systems alone.

The speakers of this conference will discuss some of the latest developments in the field of preconditioning techniques for sparse matrix problems. The conference will allow participants to exchange findings in this area and to explore possible new directions in light of emerging paradigms, such as parallel processing and object-oriented programming.

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